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Recurrent noncoding regulatory mutations in pancreatic ductal adenocarcinoma

Nature Genetics volume 49, pages 825–833 (2017)Cite this article

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Abstract

The contributions of coding mutations to tumorigenesis are relatively well known; however, little is known about somatic alterations in noncoding DNA. Here we describe GECCO (Genomic Enrichment Computational Clustering Operation) to analyze somatic noncoding alterations in 308 pancreatic ductal adenocarcinomas (PDAs) and identify commonly mutated regulatory regions. We find recurrent noncoding mutations to be enriched in PDA pathways, including axon guidance and cell adhesion, and newly identified processes, including transcription and homeobox genes. We identified mutations in protein binding sites correlating with differential expression of proximal genes and experimentally validated effects of mutations on expression. We developed an expression modulation score that quantifies the strength of gene regulation imposed by each class of regulatory elements, and found the strongest elements were most frequently mutated, suggesting a selective advantage. Our detailed single-cancer analysis of noncoding alterations identifies regulatory mutations as candidates for diagnostic and prognostic markers, and suggests new mechanisms for tumor evolution.

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Figure 1: Identification of recurrent noncoding mutations in PDA.
Figure 2: GECCO flowchart.
Figure 3: Clustered gene-proximal mutations and pathways in PDA.
Figure 4: Recurrent gene-proximal mutations correlate with gene expression changes in PDA.
Figure 5: Noncoding mutations modulate luciferase gene expression.
Figure 6: Gene-proximal NCMs are enriched in specific classes of CRRs.
Figure 7: Gene-proximal NCMs in repressors and activators cluster near distinct subsets of genes.

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Acknowledgements

We thank the members of the Tuveson laboratory, C. Vakoc and A. Siepel for discussions. D.A.T. is a distinguished scholar of the Lustgarten Foundation and Director of the Lustgarten Foundation-designated Laboratory of Pancreatic Cancer Research. D.A.T. is also supported by the Cold Spring Harbor Laboratory Association, the V Foundation, PCUK and the David Rubinstein Center for Pancreatic Cancer Research at MSKCC. In addition, we are grateful for support from the following: the STARR Foundation (I7-A718 for D.A.T.), DOD (W81XWH-13-PRCRP-IA for D.A.T.), Louis Morin Charitable Trust (M.E.F.) and NIH (5P30CA45508-26, 5P50CA101955-07, 1U10CA180944-03, 5U01CA168409-5, 1R01CA188134-01A1 and 1R01CA190092-03 for D.A.T. and R01HG006677 for M.C.S.).

Author information

Author notes

  1. John D McPherson & Sean M Grimmond

    Present address: Present addresses: University of Melbourne Centre for Cancer Research, University of Melbourne, Melbourne, Victoria, Australia (S.M.G.) and Department of Biochemistry and Molecular Medicine, UC Davis Comprehensive Cancer Center, UC Davis School of Medicine, University of California Davis, Sacramento, California, USA (J.D.M.).,

  2. Michael E Feigin and Tyler Garvin: These authors contributed equally to this work.

Authors and Affiliations

  1. Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA

    Michael E Feigin, Michael C Schatz & David A Tuveson

  2. Lustgarten Foundation Pancreatic Cancer Research Laboratory, Cold Spring Harbor, New York, USA

    Michael E Feigin & David A Tuveson

  3. Watson School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA

    Tyler Garvin

  4. Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Glasgow, Scotland, UK

    Peter Bailey, David K Chang, Sean M Grimmond & Andrew V Biankin

  5. QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Nicola Waddell

  6. Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia

    Nicola Waddell & Sean M Grimmond

  7. The Kinghorn Cancer Centre, Cancer Research Program, Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia

    David K Chang

  8. Department of Surgery, Bankstown Hospital, Bankstown, Sydney, New South Wales, Australia

    David K Chang

  9. South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Liverpool, New South Wales, Australia

    David K Chang & Andrew V Biankin

  10. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA

    David R Kelley

  11. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Shimin Shuai & Lincoln D Stein

  12. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada

    Steven Gallinger

  13. Division of General Surgery, Toronto General Hospital, Toronto, Ontario, Canada

    Steven Gallinger

  14. Genome Technologies Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada

    John D McPherson

  15. Department of Physiology and Biophysics, Sandra and Edward Meyer Cancer Center, Institute for Computational Biomedicine, Weill Medical College of Cornell University, New York, New York, USA

    Ekta Khurana

  16. Informatics and Biocomputing, Ontario Institute for Cancer Research, Toronto, Ontario, Canada

    Lincoln D Stein

  17. Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA

    Michael C Schatz

  18. Department of Biology, Johns Hopkins University, Baltimore, Maryland, USA

    Michael C Schatz

  19. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York, USA

    David A Tuveson

  20. West of Scotland Pancreatic Unit, Glasgow Royal Infirmary, Glasgow, Scotland, UK

    Andrew V Biankin

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Contributions

M.E.F., T.G., M.C.S. and D.A.T. wrote the manuscript. M.C.S. and D.A.T. supervised the study. T.G. performed FunSeq analysis and developed GECCO. M.E.F. performed pathway analysis. M.E.F., T.G., S.M.G., A.V.B., E.K., S.S., L.D.S., S.G. and J.D.M. contributed to data analysis. D.K.C. and P.B. performed patient outcome analysis. D.R.K. performed Basset analysis. N.W. performed germline sequence analysis.

Corresponding authors

Correspondence to Michael C Schatz or David A Tuveson.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Identification of recurrent noncoding mutations in PDA.

Distribution of SNV rates across the patient cohort.

Supplementary Figure 2 Overlap of SNVs and common coding mutations in PDA.

Distribution of SNVs across the patient cohort, with common coding mutations (colored bars) in PDA genes.

Supplementary Figure 3 Overlap of gene-proximal NCMs in CRRs and common coding mutations in PDA.

Distribution of CRR mutation rates across the patient cohort, with common coding mutations (colored bars) in PDA genes.

Supplementary Figure 4 NCMs disrupt transcription factor binding motifs.

(a) A G→A mutation in a regulatory site on chromosome 15 at position 25,200,056 alters a critical nucleotide in an NRF1 binding site. The regulatory site lies in the promoter of SNRPN. At the bottom, the heat map displays the predicted change in binding, considered here as ChIP-seq signal for NRF1 in H1-hESCs. The line plots above measure the maximum (gain) and minimum (loss) predicted change; the loss highlights nucleotides that significantly alter the overall signal upon mutation as this mutation does. (b) A G→T mutation in a regulatory site on chromosome 3 at position 115,757,580 introduces a GATA factor binding site nearby an established PU.1 binding site. The heat map displays the predicted change in accessibility, considered here as DNase-seq signal in K562. In other cells, such as monocytes, the model predicts reduced accessibility, suggesting that GATA binding here may alter the combinatorial logic of the regulatory element in a complex fashion.

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Supplementary Figures 1–4, Supplementary Tables 1–4 and Supplementary Note (PDF 2660 kb)

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Feigin, M., Garvin, T., Bailey, P. et al. Recurrent noncoding regulatory mutations in pancreatic ductal adenocarcinoma. Nat Genet 49, 825–833 (2017). https://doi.org/10.1038/ng.3861

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